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--- ece4580:module_surveillance:m2w2 [2017/04/03 10:34] – [Week #2: Testing the Method] pvela
+++ ece4580:module_surveillance:m2w2 [2024/08/20 21:38] (current) – external edit 127.0.0.1
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 Once we know the mixture models are proper, then the next step is to actually use the models as a means to test proximity of one model to another.  If two models are close, or the data from a model is consistent with the model, then two should be the same.  In this manner, we can identify when the same person comes in and out, or when it gets occluded and re-appears. This action is called //re-identification//.
+=== Matching Data to Model: Theory ===
 To start simply, let's first consider the case of checking the data from the model for consistency. Imagine that there are 10 people who has walked in, each with their own Gaussian mixture model as the signature, and that they have subsequently left the scene. One of those 10 returns. Which of them is it? ... Well, we have a new set of image data from the binary masked image portion of the recently entered person. Our question get converted to the question: Which of the existing models is the new data a good fit for?  That naturally begets the question: How can we create a scoring mechanism for testing fitness of data to existing models?
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 $$ r^* = \arg \min_{r} E(T; M_r). $$
+=== Matching Data to Model: Practice ===
+Based on the earlier [[ECE4580:Module_Surveillance:M2W1|module]], you should be comfortable with "vectorizing" or converting the template data to sequential form (basically into a 2D matrix where the data is column-wise, or possibly row-wise).  What is needed then is to simply score the data.  For that, Matlab has the 'mahal' member function. You should see that it returns multiple distances, one for each model in the mixture. Only the minimum value should be taken and used.  It should be somewhat clear what to do for that step and for the final summation step.
+=== Implementation ===
+  - Create a function that takes in a model and the (sequential) template data matrix, and returns the score. Call it ''scoreTarget''.
+  - Create a second function that takes in the template matrix data, plus the set of models, and returns both the index into the best scoring model (first argument) and its associated score (second argument). Call it ''matchTargetData.''  Since your model is a Gaussian mixture class instance, and you will have multiple such ones, they should be stored in an array (or at least you should do/have done something to that effect).
+  - Test out the ''matchTargetData'' function using the **exact same** data that generated the model. In practice you should get back the index to the model that the data was used to create. If you pass it in the same order as you created the models, then your ''matchTargetData'' output for the data should basically go from 1 to the number of models you have, in order.  Comment on whether this happened or not.  If some of the outputs were wrong, plot the cartoon visualization of the confused template models, plot the actual data being tested, and explain why the matching could have been mistaken.
+  - Test out the ''matchTargetData'' function using the **second template or paired data** for the person. Assess the accuracy of the output by computing (total correct) / (total test cases). Are the model assignment errors consistent with the mistakes made in the previous step when the exact same data was used? If different, is the mistake sensible or not? Support with visual and scoring evidence.
+  - Download someone else's data from the google drive location (hopefully there is more than one set of data there), and apply the same procedure to it. What this means is, use the first set of the data to train a set of Gaussian mixture models, then use the second set to test with. Report the accuracy, and answer the same questions as the previous step.
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